AUTHORS: Chris Jones, Center for Geospatial Analytics, North Carolina State University; Aaron Moody, Geography, UNC-Chapel Hill; Ross Meentemeyer, Center for Geospatial Analytics, North Carolina State University
ABSTRACT: Forest pathogens can have large-scale impacts on forest composition and can interact with natural disturbance regimes to dramatically change forest composition. But the long-term impacts of and interactions with natural disturbance regimes for newly introduced pests and pathogens are not well understood because current disease models don’t account for disease-related mortality or interactions with other disturbances. Many models of forest growth and succession work ignore the spatial distribution of species across a landscape. Therefore, we have combined a dynamic epidemiological model, a fire-behavior model, and a forest landscape simulation model (LANDIS-II) to understand how these disturbances interact and change forest composition over the course of a century using Phytophthora ramorum as our case study invasive pathogen. Three disturbance scenarios (fire, disease, and fire and disease) were used in order to understand the interacting effects of fire and disease on forest composition. The model scenarios were simulated from 1990 to 2090 using projected daily climate data. Data from these scenarios were aggregated to the entire study area and to ecoregion levels for analysis purposes. Biomass for four key species (tanoak, coast live oak, California bay laurel, and California black oak) affected by P. ramorum was analyzed over the 100-year simulation for the 3 disturbance scenarios. Our results suggest that changes in species composition are driven by asymmetries in host competency and species-specific response to fire across the study system. Additionally, initial species composition serves to either magnify or mitigate the effects of P. ramorum in the study system.
